Carbon instensity of Sustainable PV for Energy Transition¶

This analysis explores the carbon implications of different PV sustainability/circular economy designs in the context of achieving energy transition. These calculations build upon previous work that can be found in journals 13 and 17.

Attempt 1

In [1]:
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'CarbonAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
carbonfolder = str(Path().resolve().parent.parent / 'PV_ICE'/ 'baselines'/ 'CarbonLayer')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')
energyanalysisfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)
In [2]:
from platform import python_version 
print(python_version())

3.11.5
In [3]:
import PV_ICE
PV_ICE.__version__
Out[3]:
''
In [4]:
#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#000000', #PV ICE baseline
              '#595959', '#7F7F7F', '#A6A6A6', '#D9D9D9', #BAU, 4 grays, perc, shj, topcon, irena
              #'#067872','#0aa39e','#09d0cd','#00ffff', #realistic cases (4) teals, perc, shj, topcon, irena
              '#0579C1','#C00000','#FFC000', #extreme cases (3) long life, high eff, circular
                '#6E30A0','#00B3B5','#10C483', #ambitious modules (5) high eff+ long life, 50 yr perc, recycleSi, 
               '#97CB3F','#FF7E00' #circular perovskite+life, circular perovkiste+ high eff
                ] 

colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B', 'black'] #colors for material plots       

import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl

from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)

plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)

scennames_labels = ['PV_ICE','PERC','SHJ','TOPCon','Low\nQuality',
                         'Long-Lived','High Eff','Circular',
                        'High Eff\n+ Long-life','Long-Life\n+ Recycling',
                         'Recycled-Si\n+ Long-life','Circular\n+ Long-life',
                        'Circular\n+ High Eff'
                    ]  

scennames_labels_flat = ['PV_ICE','PERC','SHJ','TOPCon','Low Quality',
                         'Long-Lived','High Eff','Circular',
                        'High Eff + Long-life','Long-Life + Recycling',
                         'Recycled-Si + Long-life','Circular + Long-life',
                        'Circular + High Eff'
                    ]
In [5]:
MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')
In [6]:
#load in the simulation from Energy Analysis journal
sim1 = PV_ICE.Simulation.load_Simpickle(filename=r'C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\sim1.pkl')

sim1.calculateCarbonFlows()

In [87]:
#sim1.calculateEnergyFlow()
In [26]:
sim1.scenario['r_PERC'].dataOut_e
Out[26]:
mod_MFG mod_Install mod_OandM mod_Repair mod_Demount mod_Store mod_Resell_Certify mod_ReMFG_Disassembly mod_Recycle_Crush e_out_annual_[Wh]
0 5.595432e+10 5.724557e+07 0.0 0.0 0.000000e+00 0.0 0.0 0.0 0.000000e+00 1.378769e+12
1 1.962572e+10 1.986038e+07 0.0 0.0 3.657513e-01 0.0 0.0 0.0 1.547705e+00 1.853184e+12
2 2.379485e+10 2.382044e+07 0.0 0.0 2.825729e+01 0.0 0.0 0.0 1.195730e+02 2.425924e+12
3 3.657938e+10 3.622915e+07 0.0 0.0 3.455821e+02 0.0 0.0 0.0 1.462358e+03 3.307904e+12
4 7.544213e+10 7.393329e+07 0.0 0.0 1.984355e+03 0.0 0.0 0.0 8.396954e+03 5.136280e+12
... ... ... ... ... ... ... ... ... ... ...
96 3.042066e+12 8.530612e+09 0.0 0.0 8.530612e+09 0.0 0.0 0.0 4.286633e+11 1.296137e+16
97 3.042066e+12 8.530612e+09 0.0 0.0 8.530612e+09 0.0 0.0 0.0 4.286633e+11 1.296137e+16
98 3.042066e+12 8.530612e+09 0.0 0.0 8.530612e+09 0.0 0.0 0.0 4.286633e+11 1.296137e+16
99 3.042066e+12 8.530612e+09 0.0 0.0 8.530612e+09 0.0 0.0 0.0 4.286633e+11 1.296137e+16
100 3.042066e+12 8.530612e+09 0.0 0.0 8.530612e+09 0.0 0.0 0.0 4.286633e+11 1.296137e+16

101 rows × 10 columns

Project grid forward to 100% re in 2050¶

To parallel the PV deployment, we will assume that we globally hit 100% RE in 2050 with the 75 TW of PV. As such, we need to change the future projection of marketshares of the different country grids.

One scenario with decarb grid, one scenario with decarb grid and heat

Estimating that 60-70% generation will be from Solar, 30-40% from wind, and any remainder from "other renewables"

In [27]:
countrygridmix = pd.read_csv(os.path.join(carbonfolder,'baseline_countrygridmix.csv'), index_col='year')
gridsources = ['Bioenergy','Hydro','Nuclear','OtherFossil','OtherRenewables','Solar','Wind']
nonRE = ['Coal','Gas','OtherFossil','Nuclear','Bioenergy']
In [28]:
countrygridmix.loc[2023:,:]=np.nan #delete 2023 to 2050
nonRE_search = '|'.join(nonRE) #create nonRE search
countrygridmix.loc[2050, countrygridmix.columns.str.contains(nonRE_search)] = 0.0 #set all nonRE to 0 in 2050
In [29]:
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Solar')] = 63.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Wind')] = 33.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Hydro')] = 3.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('OtherRenewables')] = 1.0
#numbers derived from leading scenario electricity generation Breyer et al 2022 scenarios (EU focused)
In [30]:
countrygridmix_100RE2050 = countrygridmix.interpolate() #linearly interpolate between 2022 and 2050
In [31]:
apnd_idx = pd.RangeIndex(start=2051,stop=2101,step=1) #create temp df
apnd_df = pd.DataFrame(columns=countrygridmix_100RE2050.columns, index=apnd_idx )
countrygridmix_100RE20502100 = pd.concat([countrygridmix_100RE2050.loc[2000:],apnd_df], axis=0) #extend through 2100
countrygridmix_100RE20502100.ffill(inplace=True) #propogate 2050 values through 2100
In [32]:
countrygridmix_100RE20502100.loc[2050]
Out[32]:
China_Bioenergy            0.0
China_Coal                 0.0
China_Gas                  0.0
China_Hydro                3.0
China_Nuclear              0.0
                          ... 
Zambia_Nuclear             0.0
Zambia_OtherFossil         0.0
Zambia_OtherRenewables     1.0
Zambia_Solar              63.0
Zambia_Wind               33.0
Name: 2050, Length: 472, dtype: float64

This is a simple projection, assumes all countries have same ratio of PV and wind (which we know can't be true). Update in future with country specific projections.

In [33]:
pd.read_csv(os.path.join(carbonfolder,'baseline_electricityemissionfactors.csv'), index_col=[0])
Out[33]:
CO2eq_gpWh_IPCC2006 CO2eq_gpWh_ember CO2_gpWh_EIA CO2_gpWh_EPA
Energy Source
Bioenergy 0.3005 0.230 0.0000 0.3170
Coal 0.3487 0.820 0.3215 0.3380
Gas 0.2291 0.490 0.1805 0.1810
Hydro 0.0000 0.024 0.0000 0.0000
Nuclear 0.0000 0.012 0.0000 0.0000
OtherFossil 0.2671 0.700 0.2413 0.0000
OtherRenewables 0.0000 0.038 0.0000 0.0000
Solar 0.0000 0.048 0.0000 0.0000
Wind 0.0000 0.011 0.0000 0.0000
SteamAndHeat 0.0000 0.000 0.0000 0.2266
In [34]:
sim1.calculateCarbonFlows(countrygridmixes=countrygridmix_100RE20502100)


>>>> Calculating Carbon Flows <<<<

Working on Scenario:  PV_ICE
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_PERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_SHJ
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_TOPCon
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_IRENA
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_Life
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_High_eff
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_Circular
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_EffLife
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_50PERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_RecycledPERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_Perovskite_life
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_Perovskite_Eff
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
In [ ]:

Carbon Analysis¶

this will become the aggregate carbon results function

In [35]:
scenarios = sim1.scenario
In [36]:
#simply group mod and mats carbon dfs by scenario
sim_carbon_dfs = pd.DataFrame()

for scen in scenarios:
    print(scen)
    mod_carbon_scen_results = sim1.scenario[scen].dataOut_c.add_prefix(str(scen+'_'))
    
    scenmatdc = pd.DataFrame()
    for mat in MATERIALS:
        print(mat)
        mat_carbon_scen_results = sim1.scenario[scen].material[mat].matdataOut_c.add_prefix(str(scen+'_'+mat+'_')) 
        scenmatdc = pd.concat([scenmatdc,mat_carbon_scen_results], axis=1) #group all material dc
    
    scen_carbon_results = pd.concat([mod_carbon_scen_results,scenmatdc], axis=1) #append mats to mod
    sim_carbon_dfs = pd.concat([sim_carbon_dfs, scen_carbon_results], axis=1) #append all scens "raw" data

#FIX INDEX of dfs
sim_carbon_dfs.index = pd.RangeIndex(start=2000,stop=2101,step=1)
    
#return sim_carbon_results, sim_annual_carbon

PV_ICE
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_PERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_SHJ
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_TOPCon
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_IRENA
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_Life
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_High_eff
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_Circular
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_EffLife
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_50PERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_RecycledPERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_Perovskite_life
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_Perovskite_Eff
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
In [37]:
#Do math on the carbon dfs, take in the output aggregate sim df
sim_annual_carbon = pd.DataFrame()
for scen in scenarios:
    mod_mfg_carbon_total = sim_carbon_dfs.filter(like=scen).filter(like='Global_mod_MFG') #annual mfging elec carbon
    mod_nonvMFG = ['Install','OandM','Repair','Demount','Store','Resell','ReMFG','Recycle'] #could remove from loop
    nonvMFG_search = '|'.join(mod_nonvMFG) #create nonRE search
    mod_carbon_sum_nonvmfg = sim_carbon_dfs.loc[:,sim_carbon_dfs.columns.str.contains(nonvMFG_search)].filter(like=scen).filter(like='_mod_') #annual non mfging carbon
    scen_annual_carbon_mod = pd.concat([mod_mfg_carbon_total,mod_carbon_sum_nonvmfg], axis=1)
    scen_annual_carbon_mod[scen+'_Annual_Emit_mod_elec_gCO2eq'] = scen_annual_carbon_mod.sum(axis=1)

    scenmatdcmaths = pd.DataFrame()
    for mat in MATERIALS:
        scen_mat_dc_temp = sim_carbon_dfs.filter(like=scen).filter(like=mat)
        #calculation for annual carbon emissions total (selecting to avoid double countings)
        mat_global_vmfg_elec = scen_mat_dc_temp.filter(like='Global_vmfg_elec') #select global mod mfging
        mat_vmfg_countries = scen_mat_dc_temp.filter(like='vmfg_elec') #select country specific mod mfging, includes global
        mat_emit_lifecycle = scen_mat_dc_temp.loc[:,~scen_mat_dc_temp.columns.isin(mat_vmfg_countries.columns)] #select everything not the two above
        
        scen_mat_annual_carbon = pd.concat([mat_global_vmfg_elec,mat_emit_lifecycle], axis=1) #group global mod, lifecycle
        scen_mat_annual_carbon[scen+'_Annual_Emit_'+mat+'_gCO2eq'] = scen_mat_annual_carbon.sum(axis=1) #sum annual emit
        
        scenmatdcmaths = pd.concat([scenmatdcmaths,scen_mat_annual_carbon], axis=1)
        #add by material
        #add by process, fuel, elec
        
        #mat_ce_recycle = mat_carbon_scen_results.filter(like='Recycle_e_p')
        #mat_ce_remfg = mat_carbon_scen_results.filter(like='ReMFG_clean')
        #mat_landfill = mat_carbon_scen_results.filter(like='landfill_total')
        #mat_scen_annual_carbon = pd.concat([mat_vmfg_total,mat_ce_recycle,mat_ce_remfg,mat_landfill], axis=1)
    scen_modmat_annual_carbon = pd.concat([scen_annual_carbon_mod,scenmatdcmaths], axis=1)
    scen_modmat_annual_carbon[scen+'_Annual_Emit_total_modmats_gCO2eq'] = scen_modmat_annual_carbon.filter(like='Annual_Emit').sum(axis=1)
    
    sim_annual_carbon = pd.concat([sim_annual_carbon, scen_modmat_annual_carbon], axis=1)


    #FIX INDEX of dfs
sim_annual_carbon.index = pd.RangeIndex(start=2000,stop=2101,step=1)
In [38]:
#create cumulative
sim_cumu_carbon = sim_annual_carbon.cumsum()
maxy = round(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats_gCO2eq').max()/1e12,-3) #for graphing
sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats_gCO2eq')
Out[38]:
PV_ICE_Annual_Emit_total_modmats_gCO2eq               1.731339e+16
r_PERC_Annual_Emit_total_modmats_gCO2eq               1.636384e+16
r_SHJ_Annual_Emit_total_modmats_gCO2eq                1.588159e+16
r_TOPCon_Annual_Emit_total_modmats_gCO2eq             1.606521e+16
r_IRENA_Annual_Emit_total_modmats_gCO2eq              1.772711e+16
ex_Life_Annual_Emit_total_modmats_gCO2eq              1.721522e+16
ex_High_eff_Annual_Emit_total_modmats_gCO2eq          1.406457e+16
ex_Circular_Annual_Emit_total_modmats_gCO2eq          1.274909e+16
h_EffLife_Annual_Emit_total_modmats_gCO2eq            1.498071e+16
h_50PERC_Annual_Emit_total_modmats_gCO2eq             1.643084e+16
h_RecycledPERC_Annual_Emit_total_modmats_gCO2eq       1.541015e+16
h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq    1.745636e+16
h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq     1.310788e+16
Name: 2100, dtype: float64
In [ ]:

Carbon Emissions Cumulative Scenario compare¶

In [39]:
sim_cumu_carbon_mmt = sim_cumu_carbon.filter(like='Annual_Emit_total_modmats_gCO2eq')/1e12
sim_cumu_carbon_mmt
Out[39]:
PV_ICE_Annual_Emit_total_modmats_gCO2eq r_PERC_Annual_Emit_total_modmats_gCO2eq r_SHJ_Annual_Emit_total_modmats_gCO2eq r_TOPCon_Annual_Emit_total_modmats_gCO2eq r_IRENA_Annual_Emit_total_modmats_gCO2eq ex_Life_Annual_Emit_total_modmats_gCO2eq ex_High_eff_Annual_Emit_total_modmats_gCO2eq ex_Circular_Annual_Emit_total_modmats_gCO2eq h_EffLife_Annual_Emit_total_modmats_gCO2eq h_50PERC_Annual_Emit_total_modmats_gCO2eq h_RecycledPERC_Annual_Emit_total_modmats_gCO2eq h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq
2000 1.481254 1.481254 1.481254 1.481254 1.481254 1.481254 1.481254 1.481254 1.481254 1.481254 1.507282 1.481254 1.481254
2001 1.959126 1.959126 1.959126 1.959126 1.959126 1.959126 1.959126 1.959126 1.959126 1.959126 1.994211 1.959126 1.959126
2002 2.469489 2.469489 2.469489 2.469489 2.469489 2.469489 2.469489 2.469489 2.469489 2.469489 2.515655 2.469489 2.469489
2003 3.165749 3.165749 3.165749 3.165749 3.165749 3.165749 3.165749 3.165749 3.165749 3.165749 3.229215 3.165749 3.165749
2004 4.428939 4.428939 4.428939 4.428939 4.428939 4.428939 4.428939 4.428939 4.428939 4.428939 4.527112 4.428939 4.428939
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2096 17182.803324 16335.571758 15854.212043 16037.500746 17685.270799 17070.829739 13953.778500 12728.123473 14869.910894 16369.260488 15369.923262 17435.885815 13093.558258
2097 17215.449452 16342.639071 15861.057255 16044.427997 17695.730912 17106.965743 13981.477061 12733.365454 14897.609454 16384.587572 15379.980150 17441.004429 13097.139412
2098 17248.095580 16349.706383 15867.902467 16051.355247 17706.191025 17143.084065 14009.175621 12738.607436 14925.308015 16399.950900 15390.037038 17446.123043 13100.720567
2099 17280.741708 16356.773696 15874.747679 16058.282498 17716.651138 17179.175032 14036.874182 12743.849417 14953.006576 16415.363936 15400.093926 17451.241657 13104.301722
2100 17313.387836 16363.841009 15881.592891 16065.209748 17727.111251 17215.224298 14064.572743 12749.091399 14980.705137 16430.843982 15410.150814 17456.360271 13107.882877

101 rows × 13 columns

In [40]:
fig_cumu_carbon, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5), sharey=True, sharex=True, 
                                      gridspec_kw={'wspace':0})

#BAU
ax1.set_prop_cycle(color=colorpalette[0:5])
ax1.plot(sim_cumu_carbon_mmt.iloc[:,0:5], label=scennames_labels_flat[0:5]) # baselines
ax1.set_title('Business as Usual', fontsize=14)
ax1.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax1.set_xlim(2000,2100)
ax1.legend(bbox_to_anchor=(0.9,-0.05))
ax1.set_ylim(0,maxy+1000)
ax1.xaxis.set_minor_locator(MultipleLocator(10))
ax1.grid(axis='both', which='both', color='0.9', ls='--') 


#Extreme
ax2.set_prop_cycle(color=colorpalette[5:8])

ax2.plot(sim_cumu_carbon_mmt.iloc[:,5:8], label=scennames_labels_flat[5:8])
ax2.xaxis.set_minor_locator(MultipleLocator(10))
ax2.grid(axis='both', which='both', color='0.9', ls='--') 

#create glowlines for Extreme scens
n_lines = 10
diff_linewidth = 1.05
alpha_value = 0.05
for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,5],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[5])

for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,6],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[6])

for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,7],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[7])

ax2.set_title('Extreme', fontsize=14)
ax2.legend(bbox_to_anchor=(0.9,-0.05))

#Ambitious
ax3.set_prop_cycle(color=colorpalette[8:])
ax3.plot(sim_cumu_carbon_mmt.iloc[:,8:], label=scennames_labels_flat[8:], ls='--')# 
ax3.set_title('Ambitious', fontsize=14)
ax3.legend(bbox_to_anchor=(1.05,-0.05)) #(x,y)
ax3.xaxis.set_minor_locator(MultipleLocator(10))
ax3.grid(axis='both', which='both', color='0.9', ls='--') 

#overall figure

fig_cumu_carbon.suptitle('Cumulative Carbon Emissions', fontsize=24, y=1)
plt.show()
#fig_cumu_carbon.savefig('energyresults-annualMatDemands-decade.png', dpi=300, bbox_inches='tight')

Literature Validation¶

In [41]:
#comparing to Ember open source data, uses a lifecycle PV emission factor from IPCC for electricity carbon
ember_PVCO2 = pd.read_csv(os.path.join(carbonfolder,'Ember-PVEmissionsWorld2000-2022.csv'), index_col='year')
#ember_PVCO2['emissions_mtco2'] #ANNUAL DATA
ember_PVCO2_cumu = ember_PVCO2.cumsum()
In [42]:
#compare to Fthenakis and Leccisi 2021 analysis
FL2021_gwp_scSi2020 = 1010 #kg CO2eq/kWp from Fthenakis and Leccisi 2021 "sc_Si 2020"
FL2021_gwp_scSi2015 = 2000 #"scSi 2015"
FL2021_gwp_mcSi2020 = 1087 #mcSi 2020
FL2021_gwp_mcSi2015 = 1435 #mcSi 2015

kw_installed_pvice = sim1.scenario['PV_ICE'].dataIn_m['new_Installed_Capacity_[MW]']*1000 # kW installed

FL2021_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
FL2021_gwp_range['F&L_sc-Si_2020'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_scSi2020
FL2021_gwp_range['F&L_sc-Si_2015'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_scSi2015
FL2021_gwp_range['F&L_mc-Si_2020'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_mcSi2020
FL2021_gwp_range['F&L_mc-Si_2015'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_mcSi2015

FL2021_gwp_range_cumu_mmt = FL2021_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [43]:
#compare to Ultra Low Carbon Solar Alliance South Korea rating, as redproduced in Polverini 2023
Polverini2023_low = 550 #kg CO2eq/kWp "France"
Polverini2023_high = 762 #kg CO2eq/kWp "China"

kw_installed_pvice = sim1.scenario['PV_ICE'].dataIn_m['new_Installed_Capacity_[MW]']*1000 # kW installed

Polverini2023_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
Polverini2023_gwp_range['Polverini2023_low'] = kw_installed_pvice.loc[:22].values*Polverini2023_low
Polverini2023_gwp_range['Polverini2023_high'] = kw_installed_pvice.loc[:22].values*Polverini2023_high

Polverini2023_gwp_range_cumu_mmt = Polverini2023_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [44]:
#compare to Liang and You 2023, using Figure 1 2020 values from a and e
LiangYou2023_scSi_low = 250 #kg CO2eq/m2 ""
LiangYou2023_scSi_high = 350 #kg CO2eq/m2 ""
LiangYou2023_mcSi_low = 210
LiangYou2023_mcSi_high = 300

m2_installed_pvice = sim1.scenario['PV_ICE'].dataOut_m['Area'] # area deployed in m2

LiangYou2023_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
LiangYou2023_gwp_range['LiangYou2023_scSi_low'] = m2_installed_pvice.loc[:22].values*LiangYou2023_scSi_low
LiangYou2023_gwp_range['LiangYou2023_scSi_high'] = m2_installed_pvice.loc[:22].values*LiangYou2023_scSi_high
LiangYou2023_gwp_range['LiangYou2023_mcSi_low'] = m2_installed_pvice.loc[:22].values*LiangYou2023_mcSi_low
LiangYou2023_gwp_range['LiangYou2023_mcSi_high'] = m2_installed_pvice.loc[:22].values*LiangYou2023_mcSi_high

LiangYou2023_gwp_range_cumu_mmt = LiangYou2023_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [45]:
plt.plot(sim_cumu_carbon_mmt.loc[:2022,'PV_ICE_Annual_Emit_total_modmats_gCO2eq'], label='PV_ICE', color='black')

plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_scSi_low'], label='LiangYou2023_scSi_low', color='violet', ls='-.')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_scSi_high'], label='LiangYou2023_scSi_high', color='mediumorchid', ls='-.')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_mcSi_low'], label='LiangYou2023_mcSi_low', color='darkviolet', ls=':')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_mcSi_high'], label='LiangYou2023_mcSi_high', color='blueviolet', ls=':')

plt.plot(FL2021_gwp_range_cumu_mmt['F&L_sc-Si_2020'], label='FthenakisLeccisi2021_sc-Si_2020', color='lightcoral', ls='-.')
plt.plot(FL2021_gwp_range_cumu_mmt.loc[:2015,'F&L_sc-Si_2015'], label='FthenakisLeccisi2021_sc-Si_2015', color='indianred', ls='-.')
plt.plot(FL2021_gwp_range_cumu_mmt['F&L_mc-Si_2020'], label='FthenakisLeccisi2021_mc-Si_2020', color='firebrick', ls='dotted')
plt.plot(FL2021_gwp_range_cumu_mmt.loc[:2015,'F&L_mc-Si_2015'], label='FthenakisLeccisi2021_mc-Si_2015', color='maroon', ls='dotted')

plt.plot(Polverini2023_gwp_range_cumu_mmt['Polverini2023_low'], label='Polverini2023_low', color='deepskyblue', ls='--')
plt.plot(Polverini2023_gwp_range_cumu_mmt['Polverini2023_high'], label='Polverini2023_high', color='dodgerblue', ls='--')

plt.plot(ember_PVCO2_cumu.index, ember_PVCO2_cumu['emissions_mtco2'], label='Ember_electricity', color='green', ls='--')

plt.ylabel('Cumulative Carbon\n[$CO_{2eq}$ million metric tonnes]')
plt.title('Cumulative Carbon Emissions from PV')
plt.xlim(2000,2025)
plt.ylim(0,)
plt.legend(loc='upper left', fontsize=12)
Out[45]:
<matplotlib.legend.Legend at 0x26528fd35d0>

Flip it, compare on CO2eq/kWp¶

This is a simple way of doing it, will not work for much beyond 2022, because the annual emissions include end of life of other systems, not just the mfging of installed - its not necessarily fair. Might work out on balance, kinda

In [46]:
#lit factors into scatter points
litfactors = pd.DataFrame(index=ember_PVCO2_cumu.index)

litfactors.loc[2022,'Polverini2023_low'] = Polverini2023_low
litfactors.loc[2022,'Polverini2023_high'] = Polverini2023_high
litfactors.loc[2020,'FthenakisLeccisi2021_scSi'] = FL2021_gwp_scSi2020 
litfactors.loc[2015,'FthenakisLeccisi2021_scSi'] = FL2021_gwp_scSi2015 
litfactors.loc[2020,'FthenakisLeccisi2021_mcSi'] = FL2021_gwp_mcSi2020
litfactors.loc[2015,'FthenakisLeccisi2021_mcSi'] = FL2021_gwp_mcSi2015

litfactors.loc[2020,'LiangYou2023_scSi_m$^{2}$'] = LiangYou2023_scSi_low #kg CO2eq/m2 ""
litfactors.loc[2019,'LiangYou2023_scSi_m$^{2}$'] = LiangYou2023_scSi_high  #kg CO2eq/m2 ""
litfactors.loc[2020,'LiangYou2023_mcSi_m$^{2}$'] = LiangYou2023_mcSi_low 
litfactors.loc[2019,'LiangYou2023_mcSi_m$^{2}$'] = LiangYou2023_mcSi_high

litfactors.loc[2015,'Anctil2021_low'] = 1010
litfactors.loc[2015,'Anctil2021_high'] = 1775
litfactors.loc[2020,'Anctil2021_low'] = 500
litfactors.loc[2020,'Anctil2021_high'] = 750

litfactors.loc[2005,'Jungbluth2005_scSi_m$^{2}$'] = 170 #scSi CO2fossil, might be just module not full lifecycle

litfactors.loc[2020,'Wikoff2022_low_m$^{2}$'] = 125
litfactors.loc[2020,'Wikoff2022_high_m$^{2}$'] = 275


#litfactors
In [47]:
#calculate CO2eq/kWp by dividing annual CO2eq/deployed PV
installs = pd.DataFrame(kw_installed_pvice.loc[:22])
installs.index = ember_PVCO2_cumu.index

pvice_emit_annual = sim_annual_carbon.filter(like='PV_ICE').filter(like='Annual_Emit_total_modmats_gCO2eq').loc[:2022]
pvice_annual_kgco2pkwp = pvice_emit_annual['PV_ICE_Annual_Emit_total_modmats_gCO2eq'].div(installs['new_Installed_Capacity_[MW]'], axis=0)/1e3
#pvice_annual_kgco2pkwp
In [48]:
#calculate CO2eq/m2 for PV ICE
meters2installs = pd.DataFrame(m2_installed_pvice.loc[:22])
meters2installs.index = ember_PVCO2_cumu.index

pvice_annual_kgco2eqpm2 = pvice_emit_annual['PV_ICE_Annual_Emit_total_modmats_gCO2eq'].div(meters2installs['Area'], axis=0)/1e3
#pvice_annual_kgco2eqpm2
In [49]:
litfactors.columns
Out[49]:
Index(['Polverini2023_low', 'Polverini2023_high', 'FthenakisLeccisi2021_scSi',
       'FthenakisLeccisi2021_mcSi', 'LiangYou2023_scSi_m$^{2}$',
       'LiangYou2023_mcSi_m$^{2}$', 'Anctil2021_low', 'Anctil2021_high',
       'Jungbluth2005_scSi_m$^{2}$', 'Wikoff2022_low_m$^{2}$',
       'Wikoff2022_high_m$^{2}$'],
      dtype='object')
In [50]:
#graphing

plt.plot(pvice_annual_kgco2pkwp, label='PV_ICE kg CO$_{2}$eq/kW$_{p}$', color='black')

plt.scatter(litfactors.index, litfactors['Polverini2023_low'], label='Polverini2023_low', color='deepskyblue')
plt.scatter(litfactors.index, litfactors['Polverini2023_high'], label='Polverini2023_high', color='dodgerblue')

plt.scatter(litfactors.index, litfactors['FthenakisLeccisi2021_scSi'], label='FthenakisLeccisi2021_scSi', color='lightcoral')
plt.scatter(litfactors.index, litfactors['FthenakisLeccisi2021_mcSi'], label='FthenakisLeccisi2021_mcSi', color='firebrick')

plt.scatter(litfactors.index, litfactors['Anctil2021_low'], label='Anctil2021_low', color='green')
plt.scatter(litfactors.index, litfactors['Anctil2021_high'], label='Anctil2021_high', color='limegreen')


#plt.plot(pvice_annual_kgco2eqpm2, label='PV_ICE kg CO$_{2}$eq/m$^{2}$', color='darkgray', marker='^')

#plt.scatter(litfactors.index, litfactors['LiangYou2023_scSi_m$^{2}$'], label='LiangYou2023_scSi_m$^{2}$', color='violet', marker='^')
#plt.scatter(litfactors.index, litfactors['LiangYou2023_mcSi_m$^{2}$'], label='LiangYou2023_mcSi_m$^{2}$', color='darkviolet', marker='^')

#plt.scatter(litfactors.index, litfactors['Jungbluth2005_scSi_m$^{2}$'], label='Jungbluth2005_scSi_m$^{2}$', color='orange', marker='^')
plt.ylim(0,2500)
plt.ylabel('kg CO$_{2}$eq/kW$_{p}$ OR kg CO$_{2}$eq/m$^{2}$')
plt.title('Literature Comparison:\nkg CO$_{2}$eq/kW$_{p}$')
plt.legend(bbox_to_anchor=(1.6,1))
Out[50]:
<matplotlib.legend.Legend at 0x26528fc2a10>
In [51]:
plt.plot(pvice_annual_kgco2eqpm2, label='PV_ICE kg CO$_{2}$eq/m$^{2}$', color='darkgray', marker='^')

plt.scatter(litfactors.index, litfactors['LiangYou2023_scSi_m$^{2}$'], label='LiangYou2023_scSi_m$^{2}$', color='violet', marker='^')
plt.scatter(litfactors.index, litfactors['LiangYou2023_mcSi_m$^{2}$'], label='LiangYou2023_mcSi_m$^{2}$', color='darkviolet', marker='^')

plt.scatter(litfactors.index, litfactors['Jungbluth2005_scSi_m$^{2}$'], label='Jungbluth2005_scSi_m$^{2}$', color='orange', marker='^')

plt.scatter(litfactors.index, litfactors['Wikoff2022_low_m$^{2}$'], label='Wikoff2022_low_m$^{2}$', color='fuchsia', marker='^')
plt.scatter(litfactors.index, litfactors['Wikoff2022_high_m$^{2}$'], label='Wikoff2022_high_m$^{2}$', color='deeppink', marker='^')

plt.ylim(0,375)
plt.ylabel('kg CO$_{2}$eq/m$^{2}$')
plt.title('Literature Comparison:\nkg CO$_{2}$eq/m$^{2}$')
plt.legend(bbox_to_anchor=(1.6,1))
Out[51]:
<matplotlib.legend.Legend at 0x265294f5fd0>

Contextualize versus Global Carbon Emissions and Budget¶

In [52]:
annualco2emitglobal_raw = pd.read_csv(os.path.join(carbonfolder,'WorldInData-annual-co2-emissions-per-country.csv'))
annualco2emitglobal_subset = annualco2emitglobal_raw.loc[(annualco2emitglobal_raw['Entity']=='World')&(annualco2emitglobal_raw['Year']>=2000), 'Year':]
annualco2emitglobal_subset.index=annualco2emitglobal_subset['Year']

#create % PV emit vs world
pvice_annualPVemit_bmt = sim_annual_carbon.loc[:2022,'PV_ICE_Annual_Emit_total_modmats_gCO2eq']/1e15 #bmt
world_annual_bmt = annualco2emitglobal_subset.iloc[:,1]/1e9 #bmt

percentPVemitvsWorld = pvice_annualPVemit_bmt/world_annual_bmt*100
In [53]:
#cumulative compare
sim_cumu_carbon_bmt = sim_cumu_carbon_mmt.loc[:,'PV_ICE_Annual_Emit_total_modmats_gCO2eq']/1e3
sim_cumu_carbon_bmt
Out[53]:
2000     0.001481
2001     0.001959
2002     0.002469
2003     0.003166
2004     0.004429
          ...    
2096    17.182803
2097    17.215449
2098    17.248096
2099    17.280742
2100    17.313388
Name: PV_ICE_Annual_Emit_total_modmats_gCO2eq, Length: 101, dtype: float64
In [54]:
fig_worldcompare, ax1 = plt.subplots()

ax1.plot(annualco2emitglobal_subset.iloc[:,1]/1e9, label = 'World Emissions', color='red') #bmt
ax1.plot(sim_annual_carbon.loc[:2022,'PV_ICE_Annual_Emit_total_modmats_gCO2eq']/1e15,
         label='PV_ICE PV Emissions', color='black')
ax1.set_ylim(0,40)
ax1.set_ylabel('Emissions\n[billion metric tonnes $CO_{2eq}$]')
ax1.set_xlim(2000,2023)

ax2 = ax1.twinx()
ax2.plot(percentPVemitvsWorld,ls=':', color='blue')
ax2.set_ylabel('PV Percent of World Emissions [%]', color='blue')
ax2.set_ylim(0,0.5)
#ax2.set_xlim(2000,2022)

plt.title('Compare PV Emissions to Global Emissions')
ax1.legend(loc='center left')
plt.show()
In [55]:
fig_worldcompare, ax1 = plt.subplots()

ax1.plot(annualco2emitglobal_subset.iloc[:,1]/1e9, label = 'World Annual Emissions', color='red') #bmt
ax1.plot(sim_cumu_carbon_bmt, label='PV_ICE Cumulative Emissions', color='black')
ax1.set_ylim(0,40)
ax1.set_ylabel('Emissions\n[billion metric tonnes $CO_{2eq}$]')
ax1.set_xlim(2000,2100)

#ax2 = ax1.twinx()
#ax2.plot(percentPVemitvsWorld,ls=':', color='blue')
#ax2.set_ylabel('PV Percent of World Emissions [%]', color='blue')
#ax2.set_ylim(0,0.5)
#ax2.set_xlim(2000,2022)

plt.title('Compare:\nPV Cumulative Emissions\nvs Global Annual Emissions')
ax1.legend(loc='upper right')
plt.show()
In [ ]:

Cabon Emissions by material or module¶

In [56]:
for scen in scenarios:

    scen_annual_carbon = sim_annual_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
    
    plt.plot([],[],color=colormats[0], label=MATERIALS[0])
    plt.plot([],[],color=colormats[1], label=MATERIALS[1])
    plt.plot([],[],color=colormats[2], label=MATERIALS[2])
    plt.plot([],[],color=colormats[3], label=MATERIALS[3])
    plt.plot([],[],color=colormats[4], label=MATERIALS[4])
    plt.plot([],[],color=colormats[5], label=MATERIALS[5])
    plt.plot([],[],color=colormats[6], label=MATERIALS[6])
    plt.plot([],[],color=colormats[7], label='module')


    plt.stackplot(scen_annual_carbon.index,
                  scen_annual_carbon[scen+'_Annual_Emit_glass_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_silver_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_mod_elec_gCO2eq'],
                  colors = colormats)
    plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
    plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.45,1))

#plt.legend()
    plt.show()
In [ ]:
In [57]:
maxy
Out[57]:
18000.0
In [58]:
#colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B','black'] #colors for material plots
for scen in scenarios:

    scen_cumu_carbon = sim_cumu_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
    
    plt.plot([],[],color=colormats[0], label=MATERIALS[0])
    plt.plot([],[],color=colormats[1], label=MATERIALS[1])
    plt.plot([],[],color=colormats[2], label=MATERIALS[2])
    plt.plot([],[],color=colormats[3], label=MATERIALS[3])
    plt.plot([],[],color=colormats[4], label=MATERIALS[4])
    plt.plot([],[],color=colormats[5], label=MATERIALS[5])
    plt.plot([],[],color=colormats[6], label=MATERIALS[6])
    plt.plot([],[],color=colormats[7], label='module')


    plt.stackplot(scen_cumu_carbon.index,
                  scen_cumu_carbon[scen+'_Annual_Emit_glass_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_silver_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_mod_elec_gCO2eq'],
                  colors = colormats)
    plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
    plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,maxy+1000)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.45,1))
    plt.grid(axis='both', which='both', color='0.9', ls='--')
#plt.legend()
    plt.show()
In [59]:
sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_mod_elec')
Out[59]:
PV_ICE_Annual_Emit_mod_elec_gCO2eq               1.212623e+14
r_PERC_Annual_Emit_mod_elec_gCO2eq               1.176445e+14
r_SHJ_Annual_Emit_mod_elec_gCO2eq                1.140823e+14
r_TOPCon_Annual_Emit_mod_elec_gCO2eq             1.154151e+14
r_IRENA_Annual_Emit_mod_elec_gCO2eq              1.384492e+14
ex_Life_Annual_Emit_mod_elec_gCO2eq              1.176026e+14
ex_High_eff_Annual_Emit_mod_elec_gCO2eq          9.591719e+13
ex_Circular_Annual_Emit_mod_elec_gCO2eq          1.499357e+14
h_EffLife_Annual_Emit_mod_elec_gCO2eq            1.054322e+14
h_50PERC_Annual_Emit_mod_elec_gCO2eq             1.176252e+14
h_RecycledPERC_Annual_Emit_mod_elec_gCO2eq       4.131835e+14
h_Perovskite_life_Annual_Emit_mod_elec_gCO2eq    1.504994e+14
h_Perovskite_Eff_Annual_Emit_mod_elec_gCO2eq     1.343677e+14
Name: 2100, dtype: float64
In [60]:
#create a df from which to do a bar chart of 2100 emissions by mat/mod
mats_emit_2100 = pd.DataFrame() #index=scennames_labels_flat
for mat in MATERIALS:
    mat_emit_2100 = pd.Series(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_'+mat).values)
    mats_emit_2100 = pd.concat([mats_emit_2100, mat_emit_2100], axis=1)

mats_emit_2100
mats_emit_2100.columns = MATERIALS
modmats_emit_2100 = pd.concat([mats_emit_2100,pd.Series(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_mod_elec').values)], axis=1)
modmats_emit_2100.index = scennames_labels_flat
modmats_emit_2100.rename(columns={0:'module'}, inplace=True)
modmats_emit_2100_megatonne = modmats_emit_2100/1e12
modmats_emit_2100_megatonne
Out[60]:
glass silicon silver aluminium_frames copper encapsulant backsheet module
PV_ICE 4985.001857 5448.307652 29.839661 4094.858305 5.365279 1496.525375 1132.227441 121.262267
PERC 5578.692255 5393.319933 27.196685 3744.172023 5.258404 1475.013988 22.543243 117.644478
SHJ 5404.488568 5229.003200 48.366106 3628.670681 5.096390 1429.342371 22.543243 114.082332
TOPCon 5469.155881 5290.258488 44.800550 3671.593843 5.156713 1446.285929 22.543243 115.415102
Low Quality 5148.165856 5173.238230 30.583029 4231.029705 6.123050 1707.883773 1291.638447 138.449161
Long-Lived 6051.318916 5333.231049 30.098188 4179.999998 5.419339 1475.011937 22.542248 117.602623
High Eff 4943.600668 4356.612258 24.535788 3413.679939 4.415442 1203.268142 22.543316 95.917191
Circular 3999.733407 3565.725421 36.946349 3080.907840 6.724610 1886.574753 22.543316 149.935702
High Eff + Long-life 5230.392826 4689.431162 26.534052 3627.331754 4.743368 1274.296553 22.543243 105.432179
Long-Life + Recycling 5677.770408 5267.021086 29.159569 3836.454427 5.258393 1475.012451 22.542496 117.625151
Recycled-Si + Long-life 4695.649222 3675.601401 28.341953 3616.415040 6.079947 1693.382135 1281.497615 413.183500
Circular + Long-life 5827.154288 5672.720700 36.946137 3853.274123 6.723832 1886.498520 22.543316 150.499355
Circular + High Eff 3994.897619 4558.780871 32.121337 2794.351143 5.721508 1565.099428 22.543316 134.367656
In [61]:
fig_cumuemit_modmat, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[0])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silicon'],
        bottom=modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[1])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:2].sum(axis=1), color=colormats[2])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:3].sum(axis=1), color=colormats[3])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:4].sum(axis=1), color=colormats[4])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:5].sum(axis=1), color=colormats[5])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:6].sum(axis=1), color=colormats[6])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:7].sum(axis=1), color='black')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[0])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silicon'],
        bottom=modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[1])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:2].sum(axis=1), color=colormats[2])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:3].sum(axis=1), color=colormats[3])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:4].sum(axis=1), color=colormats[4])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:5].sum(axis=1), color=colormats[5])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:6].sum(axis=1), color=colormats[6])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:7].sum(axis=1), color='black')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['glass'], color=colormats[0])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silicon'],
        bottom=modmats_emit_2100_megatonne[8:]['glass'], color=colormats[1])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:2].sum(axis=1), color=colormats[2])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:3].sum(axis=1), color=colormats[3])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:4].sum(axis=1), color=colormats[4])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:5].sum(axis=1), color=colormats[5])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:6].sum(axis=1), color=colormats[6])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:7].sum(axis=1), color='black')


ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumuemit_modmat.suptitle('Cumulative Emisisons in 2100 by material', fontsize=24)
plt.show()

#fig_cumuemit_modmat.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_14008\3630820325.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_14008\3630820325.py:45: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_14008\3630820325.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
In [62]:
cumu_emit_sum = modmats_emit_2100_megatonne.sum(axis=1)
fraction_modmats_cumu_emit = modmats_emit_2100_megatonne.div(cumu_emit_sum, axis=0)*100
fraction_modmats_cumu_emit
Out[62]:
glass silicon silver aluminium_frames copper encapsulant backsheet module
PV_ICE 28.792758 31.468755 0.172350 23.651398 0.030989 8.643747 6.539607 0.700396
PERC 34.091582 32.958765 0.166200 22.880765 0.032134 9.013862 0.137763 0.718929
SHJ 34.029890 32.924929 0.304542 22.848279 0.032090 8.999994 0.141946 0.718331
TOPCon 34.043476 32.929906 0.278867 22.854316 0.032099 9.002596 0.140323 0.718416
Low Quality 29.041200 29.182635 0.172521 23.867564 0.034541 9.634304 7.286232 0.781002
Long-Lived 35.150973 30.979736 0.174835 24.280834 0.031480 8.568067 0.130944 0.683132
High Eff 35.149313 30.975788 0.174451 24.271480 0.031394 8.555312 0.160284 0.681977
Circular 31.372694 27.968467 0.289796 24.165705 0.052746 14.797719 0.176823 1.176050
High Eff + Long-life 34.914196 31.303140 0.177122 24.213358 0.031663 8.506252 0.150482 0.703786
Long-Life + Recycling 34.555562 32.055694 0.177468 23.349101 0.032003 8.977095 0.137196 0.715880
Recycled-Si + Long-life 30.471144 23.851820 0.183917 23.467746 0.039454 10.988745 8.315932 2.681242
Circular + Long-life 33.381267 32.496584 0.211649 22.073755 0.038518 10.806941 0.129141 0.862146
Circular + High Eff 30.477062 34.778926 0.245054 21.318097 0.043649 11.940139 0.171983 1.025090
In [63]:
fig_cumuemit_modmat, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['glass'], color=colormats[0])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['silicon'],
        bottom=fraction_modmats_cumu_emit[0:5]['glass'], color=colormats[1])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:2].sum(axis=1), color=colormats[2])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:3].sum(axis=1), color=colormats[3])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:4].sum(axis=1), color=colormats[4])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:5].sum(axis=1), color=colormats[5])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:6].sum(axis=1), color=colormats[6])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:7].sum(axis=1), color='black')

ax0.set_ylim(0,100)
ax0.set_ylabel('Percent Emissions by Material\n[%]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['glass'], color=colormats[0])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['silicon'],
        bottom=fraction_modmats_cumu_emit[5:8]['glass'], color=colormats[1])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:2].sum(axis=1), color=colormats[2])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:3].sum(axis=1), color=colormats[3])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:4].sum(axis=1), color=colormats[4])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:5].sum(axis=1), color=colormats[5])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:6].sum(axis=1), color=colormats[6])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:7].sum(axis=1), color='black')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['glass'], color=colormats[0])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['silicon'],
        bottom=fraction_modmats_cumu_emit[8:]['glass'], color=colormats[1])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:2].sum(axis=1), color=colormats[2])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:3].sum(axis=1), color=colormats[3])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:4].sum(axis=1), color=colormats[4])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:5].sum(axis=1), color=colormats[5])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:6].sum(axis=1), color=colormats[6])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:7].sum(axis=1), color='black')


ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumuemit_modmat.suptitle('Fraction of Emissions attribute to Module/Material', fontsize=24)
plt.show()

#fig_cumuemit_modmat.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_14008\2389923115.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_14008\2389923115.py:45: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_14008\2389923115.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
In [ ]:

Cumulative Carbon in 2050 and 2100¶

In [64]:
#mins in 2050 and 2100
cumu_carbon_2050 = sim_cumu_carbon.loc[2050].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_2100 = sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_rankings_crittime = pd.concat([cumu_carbon_2050,cumu_carbon_2100], axis=1)
cumu_carbon_rankings_crittime.index = scennames_labels_flat
cumu_carbon_rankings_crittime_bmt = cumu_carbon_rankings_crittime/1000
round(cumu_carbon_rankings_crittime_bmt,1)
Out[64]:
2050 2100
PV_ICE 15.7 17.3
PERC 15.1 16.4
SHJ 14.7 15.9
TOPCon 14.8 16.1
Low Quality 16.3 17.7
Long-Lived 15.5 17.2
High Eff 12.6 14.1
Circular 12.4 12.7
High Eff + Long-life 13.6 15.0
Long-Life + Recycling 15.4 16.4
Recycled-Si + Long-life 14.2 15.4
Circular + Long-life 17.1 17.5
Circular + High Eff 12.8 13.1
In [65]:
cumu_carbon_rankings_crittime_plot = cumu_carbon_rankings_crittime.copy()
cumu_carbon_rankings_crittime_plot['diff'] = cumu_carbon_rankings_crittime[2100]-cumu_carbon_rankings_crittime[2050]
In [66]:
fig_cumulativeemit, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
        tick_label=scennames_labels[0:5], color=colorpalette[0:5], alpha = 0.7, edgecolor='white')
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot['diff'].iloc[0:5],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
        tick_label=scennames_labels[0:5], color=colorpalette[0:5])
ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
        tick_label=scennames_labels[5:8], color=colorpalette[5:8], alpha = 0.7, edgecolor='white')
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot['diff'].iloc[5:8],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
        tick_label=scennames_labels[5:8], color=colorpalette[5:8])
ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
        tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white', alpha=0.7)
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot['diff'].iloc[8:],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
        tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white')
ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumulativeemit.suptitle('Cumulative Emissions in 2050, 2100', fontsize=24)
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

¶

Emissions by electricity vs fuels vs process¶

Process emission summing¶

This only happens on the material files

In [67]:
process_emissions = pd.DataFrame()
for scen in scenarios:
    scen_p = sim_carbon_dfs.filter(like=scen).filter(like='_p_')
    scen_p_sum = scen_p.sum(axis=1)
    process_emissions = pd.concat([process_emissions,scen_p_sum], axis=1)

process_emissions.columns = scennames_labels_flat
process_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
process_emissions_cumu = process_emissions.cumsum()
In [68]:
#process_emissions_cumu

Fuel Emissions¶

This is capturing steam and heating fuel, also only on material level

In [69]:
fuel_emissions = pd.DataFrame()
for scen in scenarios:
    scen_f = sim_carbon_dfs.filter(like=scen).filter(like='_fuel_')
    scen_f_sum = scen_f.sum(axis=1)
    fuel_emissions = pd.concat([fuel_emissions,scen_f_sum], axis=1)
    
fuel_emissions.columns = scennames_labels_flat
fuel_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
fuel_emissions_cumu = fuel_emissions.cumsum()
In [70]:
#fuel_emissions_cumu

Electricity Emissions¶

both module and material level elec.

In [71]:
elec_emissions = pd.DataFrame()
for scen in scenarios:
    scen_mod_elec = sim_carbon_dfs.filter(like=scen).filter(like='Global_mod_MFG') #module elec lifecycle emits
    
    #material elec emits
    mat_eleckey = ['Global_vmfg_elec','landfill_elec','ReMFG_clean_elec','Recycled_LQ_elec','Recycled_HQ_elec']
    mat_elecs_search = '|'.join(mat_eleckey)
    scen_mat_elecs = sim_carbon_dfs.loc[:,sim_carbon_dfs.columns.str.contains(mat_elecs_search)].filter(like=scen)
    scen_mat_elecs_sum = scen_mat_elecs.sum(axis=1)
    
    #sum them together by scen
    scen_elec_modmat_annual_sum = scen_mat_elecs_sum+scen_mod_elec.iloc[:,0]
    elec_emissions = pd.concat([elec_emissions,scen_elec_modmat_annual_sum], axis=1)
    
elec_emissions.columns=scennames_labels_flat
elec_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
elec_emissions_cumu = elec_emissions.cumsum()
In [72]:
#graphing by emission source
efp_emit_total = elec_emissions+fuel_emissions+process_emissions
efp_emit_total_cumu = elec_emissions_cumu+fuel_emissions_cumu+process_emissions_cumu
In [73]:
#graphing by emission source, annual
#efp_emit_total = elec_emissions+fuel_emissions+process_emissions

for scen in scennames_labels_flat:
    
    plt.plot([],[],color='black', label='process')
    plt.plot([],[],color='darkred', label='fuel')
    plt.plot([],[],color='blue', label='electricity')

    plt.stackplot(elec_emissions.index,
                  process_emissions[scen]/1e12, 
                  fuel_emissions[scen]/1e12,
                  elec_emissions[scen]/1e12, 
                  colors = ['black','darkred','blue'])
    plt.title(scen+':\nGHG Emissions Annually by Source')
    plt.ylabel('GHG Emissions Annually from Lifecycle Source\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))
    #plt.grid(axis='both', which='both', color='0.9', ls='--', zorder=0)
    #plt.set_axisbelow(True)
#plt.legend()
    plt.show()
In [74]:
#graphing by emission source, cumulative

for scen in scennames_labels_flat:
    
    plt.plot([],[],color='black', label='process')
    plt.plot([],[],color='darkred', label='fuel')
    plt.plot([],[],color='blue', label='electricity')

    plt.stackplot(elec_emissions_cumu.index,
                  process_emissions_cumu[scen]/1e12, 
                  fuel_emissions_cumu[scen]/1e12,
                  elec_emissions_cumu[scen]/1e12, 
                  colors = ['black','darkred','blue'])
    plt.title(scen+':\nGHG Emissions Cumulative by Source')
    plt.ylabel('GHG Emissions Cumulatively from Lifecycle Source\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,maxy+1000)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.3,1))
    plt.grid(axis='both', which='both', color='0.9', ls='--')
#plt.legend()
    plt.show()
In [75]:
#bar chart 2050 and 2100 by scenario by emission source
emit_efp_2100_forbar = pd.concat([elec_emissions_cumu.loc[2100],fuel_emissions_cumu.loc[2100],process_emissions_cumu.loc[2100]],
                                 axis=1,keys=['electricity','fuel','process'])
In [76]:
emit_efp_2100_mmt = emit_efp_2100_forbar/1e12
In [77]:
fig_cumulativeemit, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['process'].iloc[0:5],label='process',
        tick_label=scennames_labels[0:5], color='black')

ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['fuel'].iloc[0:5],label='fuel',
        bottom=emit_efp_2100_mmt['process'].iloc[0:5], 
        tick_label=scennames_labels[0:5], color='darkred')

ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['electricity'].iloc[0:5],label='electricity',
        bottom=emit_efp_2100_mmt['process'].iloc[0:5]+emit_efp_2100_mmt['fuel'].iloc[0:5],
        tick_label=scennames_labels[0:5], color='blue')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['process'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='black')

ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['fuel'].iloc[5:8],
        bottom=emit_efp_2100_mmt['process'].iloc[5:8], 
        tick_label=scennames_labels[5:8], color='darkred')

ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['electricity'].iloc[5:8],
        bottom=emit_efp_2100_mmt['process'].iloc[5:8]+emit_efp_2100_mmt['fuel'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='blue')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['process'].iloc[8:],
        tick_label=scennames_labels[8:], color='black')

ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['fuel'].iloc[8:],
        bottom=emit_efp_2100_mmt['process'].iloc[8:], 
        tick_label=scennames_labels[8:], color='darkred')

ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['electricity'].iloc[8:],
        bottom=emit_efp_2100_mmt['process'].iloc[8:]+emit_efp_2100_mmt['fuel'].iloc[8:],
        tick_label=scennames_labels[8:], color='blue')

ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig
fig_cumulativeemit.suptitle('Cumulative Emisisons in 2100 by emission source', fontsize=24)
handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.42,1))
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')
In [ ]:

vMFG versus Circular Processes¶

3 categories for this:

  • vmfg of materials
  • CE of materials and materials
  • install/decomission requirements and I think module mfging falls into the last category
In [78]:
CEkey = ['ReMFG','Recycle_Crush','Recycled_LQ','Recycled_HQ','Resell','Repair','LQ','HQ']
CEkey_search = '|'.join(CEkey)

LinearKey = ['vMFG', 'vmfg', 'landfill']
LinearKey_search = '|'.join(LinearKey)

otherkey = ['mod_MFG','OandM','Install','Demount', 'Store'] #THIS ONE IS MISSING COLUMNS!!!
otherkey_search = '|'.join(otherkey)

scen_carbon_CE.columns

scen_carbon_linear.columns

scen_carbon_other.columns

allcolumns = list(scen_annual_carbon.columns) selectedcolumns = list(scen_carbon_other.columns)+list(scen_carbon_linear.columns)+list(scen_carbon_CE.columns) selectedcolumns

set(allcolumns).difference(selectedcolumns)

In [79]:
#subset by pathways
sim_carbon_CE = pd.DataFrame()
sim_carbon_linear = pd.DataFrame()
sim_carbon_necessary = pd.DataFrame()

for scen in scenarios:
    scen_annual_carbon = sim_annual_carbon.filter(like=scen)
    
    scen_carbon_CE = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(CEkey_search)]
    scen_carbon_CE_sum = scen_carbon_CE.sum(axis=1)
    sim_carbon_CE = pd.concat([sim_carbon_CE, scen_carbon_CE_sum], axis=1)
    
    scen_carbon_linear = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(LinearKey_search)]
    scen_carbon_linear_sum = scen_carbon_linear.sum(axis=1)
    sim_carbon_linear = pd.concat([sim_carbon_linear,scen_carbon_linear_sum ], axis=1)
    
    scen_carbon_other = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(otherkey_search)]
    scen_carbon_other_sum = scen_carbon_other.sum(axis=1)
    sim_carbon_necessary = pd.concat([sim_carbon_necessary, scen_carbon_other_sum], axis=1)
    
sim_carbon_CE.columns = scennames_labels_flat
sim_carbon_linear.columns = scennames_labels_flat
sim_carbon_necessary.columns = scennames_labels_flat

#.index = pd.RangeIndex(start=2000,stop=2101,step=1)
In [80]:
sim_carbon_CE_cumu = sim_carbon_CE.cumsum()
sim_carbon_linear_cumu = sim_carbon_linear.cumsum()
sim_carbon_necessary_cumu = sim_carbon_necessary.cumsum()
In [81]:
emit_pathway = pd.concat([sim_carbon_CE_cumu.loc[2100],sim_carbon_linear_cumu.loc[2100],sim_carbon_necessary_cumu.loc[2100]],
                                 axis=1,keys=['Circular','Linear/Virgin','Necessary'])
emit_pathway_mmt = emit_pathway/1e12
In [82]:
sim_carbon_CE_cumu
Out[82]:
PV_ICE PERC SHJ TOPCon Low Quality Long-Lived High Eff Circular High Eff + Long-life Long-Life + Recycling Recycled-Si + Long-life Circular + Long-life Circular + High Eff
2000 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08 1.243800e+08
2001 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08 1.662108e+08
2002 2.147992e+08 2.147992e+08 2.147992e+08 2.147992e+08 2.147991e+08 2.147992e+08 2.147992e+08 2.147992e+08 2.147992e+08 2.147992e+08 2.147992e+08 2.147992e+08 2.147992e+08
2003 2.862976e+08 2.862976e+08 2.862976e+08 2.862976e+08 2.862942e+08 2.862976e+08 2.862976e+08 2.862976e+08 2.862976e+08 2.862976e+08 2.862976e+08 2.862976e+08 2.862976e+08
2004 4.273243e+08 4.273243e+08 4.273243e+08 4.273243e+08 4.272930e+08 4.273243e+08 4.273243e+08 4.273243e+08 4.273243e+08 4.273243e+08 4.273243e+08 4.273243e+08 4.273243e+08
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2096 7.420225e+12 1.179466e+15 1.143291e+15 1.156837e+15 1.329943e+15 4.380807e+10 4.380807e+10 7.164395e+14 4.380807e+10 2.708795e+14 1.393553e+15 4.548003e+14 3.780384e+14
2097 7.438469e+12 1.183025e+15 1.146738e+15 1.160326e+15 1.333718e+15 4.380807e+10 4.380807e+10 7.172222e+14 4.380807e+10 2.801246e+14 1.396966e+15 4.554503e+14 3.784932e+14
2098 7.456712e+12 1.186584e+15 1.150186e+15 1.163815e+15 1.337492e+15 4.380807e+10 4.380807e+10 7.180049e+14 4.380807e+10 2.892311e+14 1.400379e+15 4.561004e+14 3.789480e+14
2099 7.474955e+12 1.190143e+15 1.153633e+15 1.167304e+15 1.341267e+15 4.380807e+10 4.380807e+10 7.187876e+14 4.380807e+10 2.981477e+14 1.403792e+15 4.567504e+14 3.794028e+14
2100 7.493198e+12 1.193701e+15 1.157081e+15 1.170792e+15 1.345041e+15 4.380807e+10 4.380807e+10 7.195702e+14 4.380807e+10 3.068082e+14 1.407205e+15 4.574005e+14 3.798576e+14

101 rows × 13 columns

In [83]:
#emit_pathway.sum(axis=1) #check that matches, we're good
emit_pathway_mmt
Out[83]:
Circular Linear/Virgin Necessary
PV_ICE 7.493198 17184.639674 121.254963
PERC 1193.701253 15052.537006 117.602749
SHJ 1157.080793 14610.471474 114.040624
TOPCon 1170.792348 14779.044015 115.373386
Low Quality 1345.041439 16243.825081 138.244730
Long-Lived 0.043808 17097.577998 117.602492
High Eff 0.043808 13968.611876 95.917059
Circular 719.570248 11879.591198 149.929953
High Eff + Long-life 0.043808 14875.229281 105.432047
Long-Life + Recycling 306.808181 16006.433244 117.602556
Recycled-Si + Long-life 1407.205333 13590.819117 412.126364
Circular + Long-life 457.400456 16849.041811 149.918004
Circular + High Eff 379.857554 12594.309670 133.715653
In [84]:
fig_emitByPathway, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Necessary'].iloc[0:5],label='Necessary',
        tick_label=scennames_labels[0:5], color='darkgray', edgecolor='black')

ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Linear/Virgin'].iloc[0:5],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[0:5], 
        tick_label=scennames_labels[0:5], color='darkorange')

ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Circular'].iloc[0:5],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[0:5]+emit_pathway_mmt['Linear/Virgin'].iloc[0:5],
        tick_label=scennames_labels[0:5], color='green')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Necessary'].iloc[5:8],label='Necessary',
        tick_label=scennames_labels[5:8], color='darkgray', edgecolor='black')

ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Linear/Virgin'].iloc[5:8],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[5:8], 
        tick_label=scennames_labels[5:8], color='darkorange')

ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Circular'].iloc[5:8],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[5:8]+emit_pathway_mmt['Linear/Virgin'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='green')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Necessary'].iloc[8:],label='Necessary',
        tick_label=scennames_labels[8:], color='darkgray', edgecolor='black')

ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Linear/Virgin'].iloc[8:],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[8:], 
        tick_label=scennames_labels[8:], color='darkorange')

ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Circular'].iloc[8:],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[8:]+emit_pathway_mmt['Linear/Virgin'].iloc[8:],
        tick_label=scennames_labels[8:], color='green')

ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig
fig_emitByPathway.suptitle('Cumulative Emissions in 2100 by CE Category', fontsize=24)
handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.5,1))
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')
In [ ]:
In [ ]:

Emissions per Capacity¶

Trying to quantify the emissions entailed in achieving energy transition target capacities. Our current calculations don't allow a good method of CO2/kWh, but we do know how much it now takes to achieve the first 75 TW then the the next 11 TW will entail a different amount of carbon. This may be a valuable comparison

In [85]:
cumu_carbon_rankings_crittime#.loc[scen,2050]
Out[85]:
2050 2100
PV_ICE 15701.443883 17313.387836
PERC 15094.493922 16363.841009
SHJ 14652.032632 15881.592891
TOPCon 14820.826914 16065.209748
Low Quality 16285.618385 17727.111251
Long-Lived 15478.865718 17215.224298
High Eff 12635.724189 14064.572743
Circular 12371.731761 12749.091399
High Eff + Long-life 13551.106835 14980.705137
Long-Life + Recycling 15401.584082 16430.843982
Recycled-Si + Long-life 14245.161435 15410.150814
Circular + Long-life 17065.120639 17456.360271
Circular + High Eff 12849.848051 13107.882877
In [86]:
cumu_carbon_rankings_crittime.index = scenarios #relabel the index for the calc
scen_carbonPERcapacity = pd.DataFrame(index=scenarios)
for scen in scenarios:
    #2050
    scen_effectiveCap_TW = sim1.scenario[scen].dataOut_m.loc[50, 'Effective_Capacity_[W]']/1e12
    scen_carbon_mmt = cumu_carbon_rankings_crittime.loc[scen,2050]
    scen_carbonPERcapacity.loc[scen, 'EffectiveCap_TW_2050'] = scen_effectiveCap_TW
    scen_carbonPERcapacity.loc[scen, 'Carbon_mmt_2050'] = scen_carbon_mmt
    scen_carbonPERcapacity.loc[scen, 'CO2pTW_2050'] = scen_carbon_mmt/scen_effectiveCap_TW
    #2100
    scen_effectiveCap_TW_2100 = sim1.scenario[scen].dataOut_m.loc[100, 'Effective_Capacity_[W]']/1e12
    scen_carbon_mmt_2100 = cumu_carbon_rankings_crittime.loc[scen,2100]
    scen_carbonPERcapacity.loc[scen, 'EffectiveCap_TW_2100'] = scen_effectiveCap_TW_2100
    scen_carbonPERcapacity.loc[scen, 'Carbon_mmt_2100'] = scen_carbon_mmt_2100
    scen_carbonPERcapacity.loc[scen, 'CO2pTW_2100'] = scen_carbon_mmt_2100/scen_effectiveCap_TW_2100
    #marginal increase between 2050 and 2100
In [ ]:
round(scen_carbonPERcapacity,0)
In [ ]:
In [ ]:
In [ ]: